import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
class ScaledDotProductAttention(nn.Module):
    ''' Scaled Dot-Product Attention '''

    def __init__(self, temperature, attn_dropout=0.1):
        super().__init__()
        self.temperature = temperature
        self.dropout = nn.Dropout(attn_dropout)

    def forward(self, q, k, v, mask=None): # q/k/v.shape: (batchSize, n_head, seqLen, dim)

        attn = torch.matmul(q / self.temperature, k.transpose(1, 2))  # attn.shape: (batchSize, n_head, seqLen, seqLen)

        if mask is not None:
            attn = attn.masked_fill(mask == 0, -1e9)

        attn = self.dropout(F.softmax(attn, dim=-1)) # attn.shape: (batchSize, n_head, seqLen, seqLen)
        output = torch.matmul(attn, v) # output.shape: (batchSize, n_head, seqLen, dim)

        return output, attn
class MultiHeadAttention(nn.Module):
    '''
        “多头”注意力模型
    '''

    def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1):

        super(MultiHeadAttention, self).__init__()

        self.n_head = n_head
        self.d_k = d_k
        self.d_v = d_v
        self.w_qs = nn.Linear(d_model, n_head * d_k)
        self.w_ks = nn.Linear(d_model, n_head * d_k)
        self.w_vs = nn.Linear(d_model, n_head * d_v)

        nn.init.normal_(self.w_qs.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_k)))
        nn.init.normal_(self.w_ks.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_k)))
        nn.init.normal_(self.w_vs.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_v)))

        self.attention = ScaledDotProductAttention(temperature=np.power(d_k, 0.5))
        self.layer_normal = nn.LayerNorm(d_model)

        self.fc = nn.Linear(n_head * d_v, d_model)
        nn.init.xavier_normal_(self.fc.weight)

        self.dropout = nn.Dropout(dropout)

    def forward(self, q, k, v, mask=None):

        d_k, d_v, n_head = self.d_k, self.d_v, self.n_head

        sz_b, len_q, _ = q.size()
        sz_b, len_k, _ = k.size()
        sz_b, len_v, _ = v.size()

        residual = q

        q = self.w_qs(q).view(sz_b, len_q, n_head, d_k)
        k = self.w_ks(k).view(sz_b, len_k, n_head, d_k)
        v = self.w_vs(v).view(sz_b, len_v, n_head, d_v)

        # (n*b) x lq x dk
        q = q.permute(2, 0, 1, 3).contiguous().view(-1, len_q, d_k)
        # (n*b) x lk x dk
        k = k.permute(2, 0, 1, 3).contiguous().view(-1, len_k, d_k)
        # (n*b) x lv x dv
        v = v.permute(2, 0, 1, 3).contiguous().view(-1, len_v, d_v)

        # mask = mask.repeat(n_head, 1, 1)  # (n*b) x .. x ..
        #
        output, attn = self.attention(q, k, v, mask=None)
        # (n_heads * batch_size) * lq * dv
        output = output.view(n_head, sz_b, len_q, d_v)
        # batch_size * len_q * (n_heads * dv)
        output = output.permute(1, 2, 0, 3).contiguous().view(sz_b, len_q, -1)
        output = self.dropout(self.fc(output))
        output = self.layer_normal(output + residual)
        return output, attn
